A Novel Weighted Fuzzy Clustering Analysis Based On AFS Theory

摘要：

In the framework of AFS(Axiomatic Fuzzy Sets) theory, We propose A novel weight fuzzy clustering algorithm, which is totally different from the traditional clustering algorithm based approaches. The novel weighted fuzzy clustering algorithm has three main advantages: Firstly, the procedures of the proposed algorithm are more transparent and understandable, and the clustering results not only have definite linguistic interpretation, but also have a weight assigned to each attribute in the cluster description to make the weights effect on the clustering reflect the importance of the attribute. Secondly, the predefined distance function and objective function are not required, and the cluster number need not be given in advance. Last, the data types of the features can be various data types or sub-preference relations, even human intuition descriptions. To evaluate the performance of the proposed weighted fuzzy clustering algorithm, we consider three well-known benchmark clustering problems-Iris data,Wine data and Wisconsin diagnostic breast cancer data.

关键词： AFS structures AFS algebras clustering analysis fuzzy descriptions

作者: Yanli Zhang Xiaodong Liu Xueying Wang

作者单位: College of Software ShenYang Normal University Shenyang, China Research Center of information and Control Dalian University of Technology Dalian, China

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-5

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）